Med-ImageTools: An open-source Python package for robust data processing pipelines and curating medical imaging data [version 3; peer review: 1 approved, 1 approved with reservations]
Background Machine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train computational models that can be implemented in clinical practice. However, processing large and complex medical imaging datasets remains an ope...
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| Published in: | F1000 research Vol. 12; p. 118 |
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| Main Authors: | , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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England
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2023
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| ISSN: | 2046-1402, 2046-1402 |
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| Abstract | Background
Machine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train computational models that can be implemented in clinical practice. However, processing large and complex medical imaging datasets remains an open challenge.
Methods
To address this issue, we developed Med-ImageTools, a new Python open-source software package to automate data curation and processing while allowing researchers to share their data processing configurations more easily, lowering the barrier for other researchers to reproduce published works.
Use cases
We have demonstrated the efficiency of Med-ImageTools across three different datasets, resulting in significantly reduced processing times.
Conclusions
The AutoPipeline feature will improve the accessibility of raw clinical datasets on public archives, such as the Cancer Imaging Archive (TCIA), the largest public repository of cancer imaging, allowing machine learning researchers to process analysis-ready formats without requiring deep domain knowledge. |
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| AbstractList | Background Machine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train computational models that can be implemented in clinical practice. However, processing large and complex medical imaging datasets remains an open challenge. Methods To address this issue, we developed Med-ImageTools, a new Python open-source software package to automate data curation and processing while allowing researchers to share their data processing configurations more easily, lowering the barrier for other researchers to reproduce published works. Use cases We have demonstrated the efficiency of Med-ImageTools across three different datasets, resulting in significantly reduced processing times. Conclusions The AutoPipeline feature will improve the accessibility of raw clinical datasets on public archives, such as the Cancer Imaging Archive (TCIA), the largest public repository of cancer imaging, allowing machine learning researchers to process analysis-ready formats without requiring deep domain knowledge. Machine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train computational models that can be implemented in clinical practice. However, processing large and complex medical imaging datasets remains an open challenge.BackgroundMachine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train computational models that can be implemented in clinical practice. However, processing large and complex medical imaging datasets remains an open challenge.To address this issue, we developed Med-ImageTools, a new Python open-source software package to automate data curation and processing while allowing researchers to share their data processing configurations more easily, lowering the barrier for other researchers to reproduce published works.MethodsTo address this issue, we developed Med-ImageTools, a new Python open-source software package to automate data curation and processing while allowing researchers to share their data processing configurations more easily, lowering the barrier for other researchers to reproduce published works.We have demonstrated the efficiency of Med-ImageTools across three different datasets, resulting in significantly reduced processing times.Use casesWe have demonstrated the efficiency of Med-ImageTools across three different datasets, resulting in significantly reduced processing times.The AutoPipeline feature will improve the accessibility of raw clinical datasets on public archives, such as the Cancer Imaging Archive (TCIA), the largest public repository of cancer imaging, allowing machine learning researchers to process analysis-ready formats without requiring deep domain knowledge.ConclusionsThe AutoPipeline feature will improve the accessibility of raw clinical datasets on public archives, such as the Cancer Imaging Archive (TCIA), the largest public repository of cancer imaging, allowing machine learning researchers to process analysis-ready formats without requiring deep domain knowledge. Machine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train computational models that can be implemented in clinical practice. However, processing large and complex medical imaging datasets remains an open challenge. To address this issue, we developed Med-ImageTools, a new Python open-source software package to automate data curation and processing while allowing researchers to share their data processing configurations more easily, lowering the barrier for other researchers to reproduce published works. We have demonstrated the efficiency of Med-ImageTools across three different datasets, resulting in significantly reduced processing times. The AutoPipeline feature will improve the accessibility of raw clinical datasets on public archives, such as the Cancer Imaging Archive (TCIA), the largest public repository of cancer imaging, allowing machine learning researchers to process analysis-ready formats without requiring deep domain knowledge. BackgroundMachine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train computational models that can be implemented in clinical practice. However, processing large and complex medical imaging datasets remains an open challenge.MethodsTo address this issue, we developed Med-ImageTools, a new Python open-source software package to automate data curation and processing while allowing researchers to share their data processing configurations more easily, lowering the barrier for other researchers to reproduce published works.Use casesWe have demonstrated the efficiency of Med-ImageTools across three different datasets, resulting in significantly reduced processing times.ConclusionsThe AutoPipeline feature will improve the accessibility of raw clinical datasets on public archives, such as the Cancer Imaging Archive (TCIA), the largest public repository of cancer imaging, allowing machine learning researchers to process analysis-ready formats without requiring deep domain knowledge. Background Machine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train computational models that can be implemented in clinical practice. However, processing large and complex medical imaging datasets remains an open challenge. Methods To address this issue, we developed Med-ImageTools, a new Python open-source software package to automate data curation and processing while allowing researchers to share their data processing configurations more easily, lowering the barrier for other researchers to reproduce published works. Use cases We have demonstrated the efficiency of Med-ImageTools across three different datasets, resulting in significantly reduced processing times. Conclusions The AutoPipeline feature will improve the accessibility of raw clinical datasets on public archives, such as the Cancer Imaging Archive (TCIA), the largest public repository of cancer imaging, allowing machine learning researchers to process analysis-ready formats without requiring deep domain knowledge. |
| Author | Kazmierski, Michal Simpson, Amber Haibe-Kains, Benjamin Peoples, Jacob Qu, Kevin Ramanathan, Vishwesh Marsilla, Joseph Nakano, Minoru Welch, Mattea Kim, Sejin |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39989910$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1148/rg.293075172 10.1038/sdata.2016.44 10.1016/j.mri.2012.05.001 10.7937/TCIA.HMQ8-J677 10.1007/s003300101100 10.1016/j.phro.2022.09.004 10.1109/CVPR.2009.5206848 10.1016/j.ijrobp.2017.12.013 10.7937/TCIA.ESHQ-4D90 10.1016/j.ijrobp.2018.01.057 10.1007/s10278-013-9622-7 10.7937/K9/TCIA.2017.8oje5q00 10.1148/ryai.2019190015 10.5281/zenodo.14766792 10.5281/ZENODO.7021436 10.1118/1.4754659 |
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| Copyright | Copyright: © 2025 Kim S et al. Copyright: © 2025 Kim S et al. This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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Machine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train... Background Machine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train... Machine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train computational... BackgroundMachine learning and AI promise to revolutionize the way we leverage medical imaging data for improving care but require large datasets to train... |
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| Title | Med-ImageTools: An open-source Python package for robust data processing pipelines and curating medical imaging data [version 3; peer review: 1 approved, 1 approved with reservations] |
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